Abstract: Individual participant data (IPD) meta-analysis (MA) is an advanced method for synthesizing research by using raw data from individual studies rather than using aggregated statistics. IPD-MA offers higher statistical power and flexibility, thereby enabling researchers to standardize measures, explore subgroup effects, and examine sources of variability often inaccessible in traditional MA. While common in clinical fields, IPD-MA remains less prevalent in psychology, partly due to challenges in data access and perceived complexity. This primer provides a nonmathematical introduction to IPD-MA, outlining its core concepts (the what), the main phases of implementation (the how), and its potential to address the credibility crisis in psychological science and offer novel insights through diverse applications (the why). We conclude with guidance on data sharing practice and infrastructure. We aim to promote IPD-MA as a transformative tool for improving cumulative knowledge consolidation, replicability, open science practices, as well as theoretical and methodological development in psychology.
Fritz et al. (Thu,) studied this question.